The world has witnessed a fast-paced digital transformation in the past decade, giving rise to all-connected environments. While the increasingly widespread availability of networks has benefited many aspects of our lives, providing the necessary infrastructure for smart autonomous systems, it has also created a large cyber attack surface. This has made real-time network intrusion detection a significant component of any computerized system. With the advances in computer hardware architectures with fast, high-volume data processing capabilities and the developments in the field of artificial intelligence, deep learning has emerged as a significant aid for achieving accurate intrusion detection, especially for zero-day attacks. In this paper, we propose a deep reinforcement learning-based approach for network intrusion detection and demonstrate its efficacy using two publicly available intrusion detection datasets, namely NSL-KDD and UNSW-NB15. The experiment results suggest that deep reinforcement learning has significant potential to provide effective intrusion detection in the increasingly complex networks of the future.
|Konular||Bilgisayar Bilimleri, Bilgi Sistemleri|
|Yayımlanma Tarihi||30 Nisan 2021|
|Başvuru Tarihi||30 Kasım 2020|
|Kabul Tarihi||26 Aralık 2020|
|Yayınlandığı Sayı||Yıl 2021, Cilt 4, Sayı 1|